4.7 | 9 ratings Rate this file 256 downloads (last 30 days) File Size: 2.74 KB File ID: #18289

Neural Network training using the Extended Kalman Filter

10 Jan 2008 (Updated 07 Feb 2008)

A function using the extended Kalman filter to train MLP neural networks

File Information
Description	The extended Kalman filter can not only estimate states of nonlinear dynamic systems from noisy measurements but also can be used to estimate parameters of a nonlinear system. A direct application of parameter estimation is to train artificial neural networks. This function and an embeded example shows a way how this can be done.
Acknowledgements	The author wishes to acknowledge the following in the creation of this submission: Learning the Extended Kalman Filter This submission has inspired the following: Neural Network training using the Unscented Kalman Filter
MATLAB release	MATLAB 7.5 (R2007b)
Other requirements	It requires the ekf function, which can be downloaded from the following link: http://www.mathworks.com/matlabcentral/fileexchange/loadFile.do?objectId=18189&objectType=FILE

Tags for This File
Everyone's Tags	extended kalman filter, fuzzy logic, neural networks, parameter estimation
Tags I've Applied
Add New Tags	Please login to tag files.

Comments and Ratings (8)
21 Feb 2008	mahendra shukla	too good
21 Feb 2008	lekouch khalid	is an intersent work
21 Jul 2008	a s
28 Jul 2008	piyush singhal	it is a good effort pl generate codes for it which can be help ful for mpc
16 Sep 2008	Devanathan M	Very nice
08 Oct 2008	x y	Great job!
05 Apr 2009	V. Poor
05 Apr 2009	Vasquez

Please login to add a comment or rating.

Updates
07 Feb 2008	update description

Tag Activity for this File
Tag	Applied By	Date/Time
fuzzy logic	Yi Cao	22 Oct 2008 09:42:26
neural networks	Yi Cao	22 Oct 2008 09:42:26
parameter estimation	Yi Cao	22 Oct 2008 09:42:26
extended kalman filter	Yi Cao	22 Oct 2008 09:42:26

Public Submission Policy

NOTICE: Any content you submit to MATLAB Central, including personal information, is not subject to the protections which may be afforded information collected under other sections of The MathWorks, Inc. Web site. You are entirely responsible for all content that you upload, post, e-mail, transmit or otherwise make available via MATLAB Central. The MathWorks does not control the content posted by visitors to MATLAB Central and, does not guarantee the accuracy, integrity, or quality of such content. Under no circumstances will The MathWorks be liable in any way for any content not authored by The MathWorks, or any loss or damage of any kind incurred as a result of the use of any content posted, e-mailed, transmitted or otherwise made available via MATLAB Central. Read the complete Disclaimer prior to use.